<p>
</p>
<p>
</p>
<p>
</p>
<p>
</p>
<p>
</p>
<p>
</p>
<h1 align="center">🔬 AFusion: AlphaFold 3 GUI & Toolkit</h1>
<p align="center">
<img src="https://img.shields.io/badge/Python-3.10-blue.svg">
<img src="https://img.shields.io/badge/Framework-Streamlit-green.svg">
<img src="https://img.shields.io/badge/Model-AlphaFold3-orange.svg">
<a href="https://pypi.org/project/afusion/">
<img src="https://img.shields.io/badge/PyPI-afusion-purple.svg">
</a>
<a href='https://alphafold3-gui.readthedocs.io/en/latest/?badge=latest'>
<img src='https://readthedocs.org/projects/alphafold3-gui/badge/?version=latest' alt='Documentation Status'>
</a>
</p>
<p align="center">
<em>AFusion</em> is a user-friendly graphical interface designed to simplify the process of AlphaFold 3 installation, prediction and visualization, making it accessible to users who prefer a GUI over command-line interactions.
</p>
**[Demo site](https://af3gui.streamlit.app/) (generate input JSON files ONLY)**
[**Usable visualization site**](https://af3vis.streamlit.app/) **(fully usable)**
## Table of Contents
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation and Running](#installation-and-running)
- [Usage](#usage)
- [Launching AFusion](#launching-afusion)
- [Using the GUI](#using-the-gui)
- [Documentation](#documentation)
- [ToDo](#todo)
- [Screenshots](#screenshots)
- [License](#license)
- [Acknowledgements](#acknowledgements)
## Features
- **🧭 Guided Installation**: GUI-based installer to simplify the installation process, easily set up the application step-by-step.
- **✨ Intuitive Interface**: Easily configure job settings, sequences, and execution parameters through a clean and modern GUI.
- **📋 Entity Management**: Add multiple entities (Protein, RNA, DNA, Ligand) with support for modifications, MSA options, and templates.
- **⚙️ Dynamic JSON Generation**: Automatically generates the required JSON input file for AlphaFold 3 based on user inputs.
- **🚀 Integrated Execution**: Run AlphaFold 3 directly from the GUI with customizable Docker execution settings.
- **🖥️ Visual Feedback**: Provides command output within the interface for monitoring and debugging.
- **🖥️ Console Output**: Track processes and debug more effectively with backend console output.
- **🧩 API for Batch Predictions**: Perform batch predictions using the AFusion API in Python scripts.
### **🌟 New Feature!**
- **AlphaFold 3 Output Analysis System**: Automatically analyze and visualize results with customizable visualizations and generate detailed PDF reports for streamlined insights.
## Prerequisites
Before using AFusion, ensure that you have the following:
1. **🐳 Docker Installed**: Docker is required to run AlphaFold 3. Install Docker from the [official website](https://www.docker.com/get-started/).
2. **🧬 AlphaFold 3 Installed**: AFusion requires AlphaFold 3 to be installed and set up on your system. Follow the installation instructions provided in the [AlphaFold 3 GitHub Repository](https://github.com/google-deepmind/alphafold3) to deploy AlphaFold 3. **Or you can run step-by-step GUI by**:
```bash
afusion install
```
3. **🐍 Python 3.10 or Higher**: AFusion is built with Python and requires Python 3.10 or higher.
## Installation and Running
1. **Install AFusion**
Run the following command in your terminal to install AFusion:
```bash
pip install afusion
```
2. **Run AFusion GUI**
After installation, you can start AFusion by running:
```bash
afusion run
```
This will launch the AFusion graphical user interface (GUI) in your default web browser.
**Please Note:**
- **🧬 AlphaFold 3 Installation**: Ensure you have correctly installed AlphaFold 3, including model parameters and required databases, following the [AlphaFold 3 Installation Guide](https://github.com/google-deepmind/alphafold3/blob/main/docs/installation.md).
- **⚙️ Docker Configuration**: After installing Docker, make sure it is running properly and that your user has permission to execute Docker commands.
- **📦 Streamlit is Included in Dependencies**: AFusion's installation will automatically install all required dependencies, including Streamlit. There's no need to install it separately.
If you encounter any issues during installation or usage, please refer to the relevant official documentation or contact us for support.
## Usage
### Launching AFusion
1. **🚀 Start the Streamlit App**
From the project directory, run:
```bash
afusion run
```
2. **🌐 Access the Application**
- The application will launch, and Streamlit will provide a local URL (e.g., `http://localhost:8501`).
- Open the provided URL in your web browser to access AFusion.
### Using the GUI
**Find more about input in [here](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md)**.
#### 1. Welcome Page
- **👋 Logo and Introduction**: You'll see the AFusion logo and a brief description.
- **📑 Navigation Sidebar**: Use the sidebar on the left to navigate to different sections of the app.
#### 2. Job Settings
- **🏷️ Job Name**: Enter a descriptive name for your job.
- **🔢 Model Seeds**: Provide integer seeds separated by commas (e.g., `1,2,3`).
#### 3. Sequences
- **🔬 Number of Entities**: Select how many entities you want to add (Proteins, RNA, DNA, Ligand).
- **📋 Entity Details**: For each entity:
- **⚛️ Entity Type**: Select the type (Protein, RNA, DNA, Ligand).
- **🆔 Entity ID**: Provide an identifier for the entity.
- **🧬 Sequence Input**: Enter the sequence information.
- **✏️ Modifications**: Optionally add modifications with their types and positions.
- **📂 MSA Options**: Choose MSA generation options and provide MSA data if applicable.
- **📜 Templates**: Optionally add template data with mmCIF content and indices.
#### 4. Bonded Atom Pairs (Optional)
- **🔗 Add Bonds**: Check the box to add bonded atom pairs.
- **⚛️ Define Bonds**: For each bond, provide details for the first and second atoms, including entity IDs, residue IDs, and atom names.
#### 5. User Provided CCD (Optional)
- **📜 User CCD Input**: Paste or enter custom CCD data in mmCIF format.
#### 6. Generated JSON
- **📄 Review JSON Content**: The application generates the JSON input file based on your entries. You can review it here.
#### 7. AlphaFold 3 Execution Settings
- **🗂️ Paths Configuration**:
- **📁 AF Input Path**: Specify the path to the AlphaFold input directory (e.g., `/home/user/af_input`).
- **📂 AF Output Path**: Specify the path to the output directory (e.g., `/home/user/af_output`).
- **📂 Model Parameters Directory**: Provide the path to the model parameters directory.
- **📂 Databases Directory**: Provide the path to the databases directory.
- **⚙️ Execution Options**:
- **🏗️ Run Data Pipeline**: Choose whether to run the data pipeline (CPU-intensive).
- **💻 Run Inference**: Choose whether to run inference (requires GPU).
#### 8. Run AlphaFold 3
- **💾 Save JSON File**: Click the "Save JSON File" button to save the generated JSON to the specified input path.
- **▶️ Run AlphaFold 3 Now**: Click the "Run AlphaFold 3 Now ▶️" button to execute the AlphaFold 3 prediction using the Docker command.
- **🔧 Docker Command**: The exact Docker command used is displayed for your reference.
- **📊 Command Output**: Execution output is displayed within the app for monitoring.
### Visualization Module 🎨📊
1. **🚀 Launch Visualization Interface**
To start the visualization module, run:
```bash
afusion visualization
```
2. **📂 Upload and Analyze Results**
- **📤 Upload Files**: Upload AlphaFold 3 output files (e.g., `.cif`, `.json`) directly in the visualization interface.
- **🔬 Visual Analysis**: Perform visual analysis for each prediction, with detailed structure displays and customizable plots.
- **📄 Web Reports**: Generate web reports like Alphafold3 Server for the analysis on demand.
3. **🔗 Integrated with Prediction GUI**
- **🎛️ Seamless Integration**: The visualization tools are also integrated into the prediction GUI. Once predictions are complete, you can switch seamlessly to the visualization tab for analysis.
## Documentation
- Full Documentation in [here](https://alphafold3-gui.readthedocs.io)
## ToDo
- [X] ~~📄 **Bulid Documentation:** Tutorial for using the AFusion API in Python scripts for batch predictions.~~
- [X] ~~♻️ **Refactor Code and Publish to PyPI**: Refactor the project code for improved modularity and maintainability, and publish the latest version to PyPI for easy installation.~~
- [X] ~~🔗 **Develop AlphaFold result analysis system**: Design and implement a comprehensive analysis pipeline for AlphaFold prediction results.~~
- [X] ~~Update the system.~~
- [ ] ⚛️ **Preset Common Small Molecules & Metal Ions**: Add a dedicated section for quick access to commonly used small molecules and metal ions.
- [ ] 🛠️ **New Tool for Chemical Small Molecules**: Develop a new tool to handle and model chemical small molecules, supporting seamless integration into the prediction pipeline.
- [X] ~~🖥️ **Add Console Output**: Implement a backend console for output to track processes and debug more effectively.~~
- [X] ~~🧩 **Create API for Batch Predictions**: Develop a standalone function API to allow users to perform batch predictions with afusion in Python scripts.~~
- [X] ~~**🧭 Create Guided Installation GUI**: To simplify the installation process.~~
# Screenshots
### Prediction GUI Interface
<details>
<summary>Click to view screenshot</summary>
#### Visualization Module
</details>
### Visualization GUI Interface
<details>
<summary>Click to view screenshot</summary>
</details>
### Installation GUI Interface
<details>
<summary>Click to view screenshot</summary>
</details>
### CLI Output
<details>
<summary>Click to view screenshot</summary>
</details>
## License
This project is licensed under the GPL3 License - see the [LICENSE](LICENSE) file for details.
## Acknowledgements
- **AlphaFold 3**: This GUI is designed to work with [AlphaFold 3](https://github.com/google-deepmind/alphafold3) by DeepMind.
- **Streamlit**: AFusion is built using [Streamlit](https://streamlit.io/), an open-source app framework for machine learning and data science teams.
- **Contributors**: Waiting for more!
---
If you encounter any issues or have suggestions for improvements, please open an [issue](https://github.com/Hanziwww/AlphaFold3-GUI/issues) or submit a [pull request](https://github.com/Hanziwww/AlphaFold3-GUI/pulls).
Happy Folding! 🧬
Raw data
{
"_id": null,
"home_page": "https://github.com/Hanziwww/AlphaFold3-GUI",
"name": "afusion",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": null,
"author": "Han Wang",
"author_email": "marspenman@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/a6/11/1638c9c3b9fab71dcab7f91920e0aff2450d7965ee414e8b46221b9a58a6/afusion-1.2.2.tar.gz",
"platform": null,
"description": "<p>\n</p>\n<p>\n</p>\n<p>\n</p>\n<p>\n</p>\n<p>\n</p>\n<p>\n</p>\n\n<h1 align=\"center\">\ud83d\udd2c AFusion: AlphaFold 3 GUI & Toolkit</h1>\n\n<p align=\"center\">\n <img src=\"https://img.shields.io/badge/Python-3.10-blue.svg\">\n <img src=\"https://img.shields.io/badge/Framework-Streamlit-green.svg\">\n <img src=\"https://img.shields.io/badge/Model-AlphaFold3-orange.svg\">\n <a href=\"https://pypi.org/project/afusion/\">\n <img src=\"https://img.shields.io/badge/PyPI-afusion-purple.svg\">\n </a>\n <a href='https://alphafold3-gui.readthedocs.io/en/latest/?badge=latest'>\n <img src='https://readthedocs.org/projects/alphafold3-gui/badge/?version=latest' alt='Documentation Status'>\n </a>\n</p>\n\n<p align=\"center\">\n <em>AFusion</em> is a user-friendly graphical interface designed to simplify the process of AlphaFold 3 installation, prediction and visualization, making it accessible to users who prefer a GUI over command-line interactions.\n</p>\n\n**[Demo site](https://af3gui.streamlit.app/) (generate input JSON files ONLY)**\n\n[**Usable visualization site**](https://af3vis.streamlit.app/) **(fully usable)**\n\n## Table of Contents\n\n- [Features](#features)\n- [Prerequisites](#prerequisites)\n- [Installation and Running](#installation-and-running)\n- [Usage](#usage)\n - [Launching AFusion](#launching-afusion)\n - [Using the GUI](#using-the-gui)\n- [Documentation](#documentation)\n- [ToDo](#todo)\n- [Screenshots](#screenshots)\n- [License](#license)\n- [Acknowledgements](#acknowledgements)\n\n## Features\n- **\ud83e\udded Guided Installation**: GUI-based installer to simplify the installation process, easily set up the application step-by-step.\n- **\u2728 Intuitive Interface**: Easily configure job settings, sequences, and execution parameters through a clean and modern GUI.\n- **\ud83d\udccb Entity Management**: Add multiple entities (Protein, RNA, DNA, Ligand) with support for modifications, MSA options, and templates.\n- **\u2699\ufe0f Dynamic JSON Generation**: Automatically generates the required JSON input file for AlphaFold 3 based on user inputs.\n- **\ud83d\ude80 Integrated Execution**: Run AlphaFold 3 directly from the GUI with customizable Docker execution settings.\n- **\ud83d\udda5\ufe0f Visual Feedback**: Provides command output within the interface for monitoring and debugging.\n- **\ud83d\udda5\ufe0f Console Output**: Track processes and debug more effectively with backend console output.\n- **\ud83e\udde9 API for Batch Predictions**: Perform batch predictions using the AFusion API in Python scripts.\n\n### **\ud83c\udf1f New Feature!**\n- **AlphaFold 3 Output Analysis System**: Automatically analyze and visualize results with customizable visualizations and generate detailed PDF reports for streamlined insights.\n\n## Prerequisites\n\nBefore using AFusion, ensure that you have the following:\n\n1. **\ud83d\udc33 Docker Installed**: Docker is required to run AlphaFold 3. Install Docker from the [official website](https://www.docker.com/get-started/).\n\n2. **\ud83e\uddec AlphaFold 3 Installed**: AFusion requires AlphaFold 3 to be installed and set up on your system. Follow the installation instructions provided in the [AlphaFold 3 GitHub Repository](https://github.com/google-deepmind/alphafold3) to deploy AlphaFold 3. **Or you can run step-by-step GUI by**:\n\n ```bash\n afusion install\n ``` \n\n3. **\ud83d\udc0d Python 3.10 or Higher**: AFusion is built with Python and requires Python 3.10 or higher.\n\n## Installation and Running\n\n1. **Install AFusion**\n\n Run the following command in your terminal to install AFusion:\n\n ```bash\n pip install afusion\n ```\n\n2. **Run AFusion GUI**\n\n After installation, you can start AFusion by running:\n\n ```bash\n afusion run\n ```\n\n This will launch the AFusion graphical user interface (GUI) in your default web browser.\n\n**Please Note:**\n\n- **\ud83e\uddec AlphaFold 3 Installation**: Ensure you have correctly installed AlphaFold 3, including model parameters and required databases, following the [AlphaFold 3 Installation Guide](https://github.com/google-deepmind/alphafold3/blob/main/docs/installation.md).\n\n- **\u2699\ufe0f Docker Configuration**: After installing Docker, make sure it is running properly and that your user has permission to execute Docker commands.\n\n- **\ud83d\udce6 Streamlit is Included in Dependencies**: AFusion's installation will automatically install all required dependencies, including Streamlit. There's no need to install it separately.\n\nIf you encounter any issues during installation or usage, please refer to the relevant official documentation or contact us for support.\n\n## Usage\n\n### Launching AFusion\n\n1. **\ud83d\ude80 Start the Streamlit App**\n\n From the project directory, run:\n\n ```bash\n afusion run\n ```\n\n2. **\ud83c\udf10 Access the Application**\n\n - The application will launch, and Streamlit will provide a local URL (e.g., `http://localhost:8501`).\n - Open the provided URL in your web browser to access AFusion.\n\n### Using the GUI\n\n**Find more about input in [here](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md)**.\n\n#### 1. Welcome Page\n\n- **\ud83d\udc4b Logo and Introduction**: You'll see the AFusion logo and a brief description.\n- **\ud83d\udcd1 Navigation Sidebar**: Use the sidebar on the left to navigate to different sections of the app.\n\n#### 2. Job Settings\n\n- **\ud83c\udff7\ufe0f Job Name**: Enter a descriptive name for your job.\n- **\ud83d\udd22 Model Seeds**: Provide integer seeds separated by commas (e.g., `1,2,3`).\n\n#### 3. Sequences\n\n- **\ud83d\udd2c Number of Entities**: Select how many entities you want to add (Proteins, RNA, DNA, Ligand).\n- **\ud83d\udccb Entity Details**: For each entity:\n - **\u269b\ufe0f Entity Type**: Select the type (Protein, RNA, DNA, Ligand).\n - **\ud83c\udd94 Entity ID**: Provide an identifier for the entity.\n - **\ud83e\uddec Sequence Input**: Enter the sequence information.\n - **\u270f\ufe0f Modifications**: Optionally add modifications with their types and positions.\n - **\ud83d\udcc2 MSA Options**: Choose MSA generation options and provide MSA data if applicable.\n - **\ud83d\udcdc Templates**: Optionally add template data with mmCIF content and indices.\n\n#### 4. Bonded Atom Pairs (Optional)\n\n- **\ud83d\udd17 Add Bonds**: Check the box to add bonded atom pairs.\n- **\u269b\ufe0f Define Bonds**: For each bond, provide details for the first and second atoms, including entity IDs, residue IDs, and atom names.\n\n#### 5. User Provided CCD (Optional)\n\n- **\ud83d\udcdc User CCD Input**: Paste or enter custom CCD data in mmCIF format.\n\n#### 6. Generated JSON\n\n- **\ud83d\udcc4 Review JSON Content**: The application generates the JSON input file based on your entries. You can review it here.\n\n#### 7. AlphaFold 3 Execution Settings\n\n- **\ud83d\uddc2\ufe0f Paths Configuration**:\n - **\ud83d\udcc1 AF Input Path**: Specify the path to the AlphaFold input directory (e.g., `/home/user/af_input`).\n - **\ud83d\udcc2 AF Output Path**: Specify the path to the output directory (e.g., `/home/user/af_output`).\n - **\ud83d\udcc2 Model Parameters Directory**: Provide the path to the model parameters directory.\n - **\ud83d\udcc2 Databases Directory**: Provide the path to the databases directory.\n\n- **\u2699\ufe0f Execution Options**:\n - **\ud83c\udfd7\ufe0f Run Data Pipeline**: Choose whether to run the data pipeline (CPU-intensive).\n - **\ud83d\udcbb Run Inference**: Choose whether to run inference (requires GPU).\n\n#### 8. Run AlphaFold 3\n\n- **\ud83d\udcbe Save JSON File**: Click the \"Save JSON File\" button to save the generated JSON to the specified input path.\n- **\u25b6\ufe0f Run AlphaFold 3 Now**: Click the \"Run AlphaFold 3 Now \u25b6\ufe0f\" button to execute the AlphaFold 3 prediction using the Docker command.\n - **\ud83d\udd27 Docker Command**: The exact Docker command used is displayed for your reference.\n - **\ud83d\udcca Command Output**: Execution output is displayed within the app for monitoring.\n\n### Visualization Module \ud83c\udfa8\ud83d\udcca\n\n1. **\ud83d\ude80 Launch Visualization Interface**\n\n To start the visualization module, run:\n\n ```bash\n afusion visualization\n ```\n\n2. **\ud83d\udcc2 Upload and Analyze Results**\n\n - **\ud83d\udce4 Upload Files**: Upload AlphaFold 3 output files (e.g., `.cif`, `.json`) directly in the visualization interface.\n - **\ud83d\udd2c Visual Analysis**: Perform visual analysis for each prediction, with detailed structure displays and customizable plots.\n - **\ud83d\udcc4 Web Reports**: Generate web reports like Alphafold3 Server for the analysis on demand.\n\n3. **\ud83d\udd17 Integrated with Prediction GUI**\n\n - **\ud83c\udf9b\ufe0f Seamless Integration**: The visualization tools are also integrated into the prediction GUI. Once predictions are complete, you can switch seamlessly to the visualization tab for analysis.\n\n## Documentation\n- Full Documentation in [here](https://alphafold3-gui.readthedocs.io)\n\n## ToDo\n\n- [X] ~~\ud83d\udcc4 **Bulid Documentation:** Tutorial for using the AFusion API in Python scripts for batch predictions.~~\n- [X] ~~\u267b\ufe0f **Refactor Code and Publish to PyPI**: Refactor the project code for improved modularity and maintainability, and publish the latest version to PyPI for easy installation.~~\n- [X] ~~\ud83d\udd17 **Develop AlphaFold result analysis system**: Design and implement a comprehensive analysis pipeline for AlphaFold prediction results.~~\n - [X] ~~Update the system.~~\n- [ ] \u269b\ufe0f **Preset Common Small Molecules & Metal Ions**: Add a dedicated section for quick access to commonly used small molecules and metal ions. \n- [ ] \ud83d\udee0\ufe0f **New Tool for Chemical Small Molecules**: Develop a new tool to handle and model chemical small molecules, supporting seamless integration into the prediction pipeline. \n- [X] ~~\ud83d\udda5\ufe0f **Add Console Output**: Implement a backend console for output to track processes and debug more effectively.~~\n- [X] ~~\ud83e\udde9 **Create API for Batch Predictions**: Develop a standalone function API to allow users to perform batch predictions with afusion in Python scripts.~~\n- [X] ~~**\ud83e\udded Create Guided Installation GUI**: To simplify the installation process.~~\n\n# Screenshots\n\n### Prediction GUI Interface\n<details>\n <summary>Click to view screenshot</summary>\n \n \n #### Visualization Module\n \n</details>\n\n### Visualization GUI Interface\n\n<details>\n <summary>Click to view screenshot</summary>\n \n \n</details>\n\n### Installation GUI Interface\n<details>\n <summary>Click to view screenshot</summary>\n \n \n</details>\n\n### CLI Output\n<details>\n <summary>Click to view screenshot</summary>\n \n \n</details>\n\n## License\n\nThis project is licensed under the GPL3 License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgements\n\n- **AlphaFold 3**: This GUI is designed to work with [AlphaFold 3](https://github.com/google-deepmind/alphafold3) by DeepMind.\n- **Streamlit**: AFusion is built using [Streamlit](https://streamlit.io/), an open-source app framework for machine learning and data science teams.\n- **Contributors**: Waiting for more!\n\n---\n\nIf you encounter any issues or have suggestions for improvements, please open an [issue](https://github.com/Hanziwww/AlphaFold3-GUI/issues) or submit a [pull request](https://github.com/Hanziwww/AlphaFold3-GUI/pulls).\n\nHappy Folding! \ud83e\uddec\n\n",
"bugtrack_url": null,
"license": null,
"summary": "AFusion: AlphaFold 3 GUI & Toolkit with Visualization",
"version": "1.2.2",
"project_urls": {
"Homepage": "https://github.com/Hanziwww/AlphaFold3-GUI"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "25925c4a6d1fa3e4c7e4cf89ff499b4629dd2cfacf98c0b5b19ec156e3d20650",
"md5": "7928e897c489382521e446f8ade04033",
"sha256": "9cc139074828c22db36265c353ca06b5e6966e00ae5a73f62ebf571cc1550af4"
},
"downloads": -1,
"filename": "afusion-1.2.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7928e897c489382521e446f8ade04033",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 41839,
"upload_time": "2024-11-23T07:15:37",
"upload_time_iso_8601": "2024-11-23T07:15:37.541100Z",
"url": "https://files.pythonhosted.org/packages/25/92/5c4a6d1fa3e4c7e4cf89ff499b4629dd2cfacf98c0b5b19ec156e3d20650/afusion-1.2.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a6111638c9c3b9fab71dcab7f91920e0aff2450d7965ee414e8b46221b9a58a6",
"md5": "7d2c0c905b5b7dfd83c52dbf7d783811",
"sha256": "feb95c505d0dfc9e924d85cf4e1821aff98189e3cdf484b09e722686bf2996a2"
},
"downloads": -1,
"filename": "afusion-1.2.2.tar.gz",
"has_sig": false,
"md5_digest": "7d2c0c905b5b7dfd83c52dbf7d783811",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 57898,
"upload_time": "2024-11-23T07:15:43",
"upload_time_iso_8601": "2024-11-23T07:15:43.001383Z",
"url": "https://files.pythonhosted.org/packages/a6/11/1638c9c3b9fab71dcab7f91920e0aff2450d7965ee414e8b46221b9a58a6/afusion-1.2.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-23 07:15:43",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Hanziwww",
"github_project": "AlphaFold3-GUI",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "afusion"
}
JSON
